The rapid spread of malware in the digital world is creating a major threat to the normal operation of digital systems, demanding advanced and automated detection and classification methodologies. Recent and dangerous malware attacks, such as BlackCat (AlphV) ransomware in 2024, Lockbit in 2023 and Lumma malware in 2024, which have affected major companies in the world with millions of dollars lost, have shown the need to exploit new methods to countermeasures to this type of attack. Compared to traditional techniques such as signature-based detection and heuristic analysis, Machine learning and Deep learning have emerged as vital tools for detecting and classifying malware. This research paper is a comprehensive survey of the current state-of-the-art machine learning-based and deep learning-based malware detection and classification techniques, proposing various algorithms and methods for detecting malicious software and enumerating their respective results to combat constantly emerging malware. This review intends to present researchers with a better view of malware detection tendencies and identify emerging research directions that tackle this ever-changing threat.